{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear_2L(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(Linear_2L, self).__init__()\n",
    "        \n",
    "        n_hid = 1200\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        \n",
    "        self.fc1 = nn.Linear(input_dim, n_hid)\n",
    "        self.fc2 = nn.Linear(n_hid, n_hid)\n",
    "        self.fc3 = nn.Linear(n_hid, output_dim)\n",
    "        \n",
    "        # choose your non linearity\n",
    "        #self.act = nn.Tanh()\n",
    "        #self.act = nn.Sigmoid()\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "        #self.act = nn.ELU(inplace=True)\n",
    "        #self.act = nn.SELU(inplace=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        \n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x = self.fc1(x)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x = self.fc2(x)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y = self.fc3(x)\n",
    "        \n",
    "        return y\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "class Net(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, batch_size=128, weight_decay=0):\n",
    "        super(Net, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.channels_in = channels_in\n",
    "        self.weight_decay = weight_decay\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = Linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.5, weight_decay=self.weight_decay)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        out = self.model(x)\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "            \n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        # out: (batch_size, out_channels, out_caps_dims)\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err\n",
    "\n",
    "    def eval(self, x, y, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def get_weight_samples(self):\n",
    "        weight_vec = []\n",
    "        \n",
    "        state_dict = self.model.state_dict()\n",
    "        \n",
    "        for key in state_dict.keys():\n",
    "            \n",
    "            if 'weight' in key:\n",
    "                weight_mtx = state_dict[key].cpu().data\n",
    "                for weight in weight_mtx.view(-1):\n",
    "                    weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 2.40M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 0/10, Jtr_pred = 0.234868, err = 0.070383, \u001b[31m   time: 3.802101 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.119621, err = 0.038100\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_regular_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type Linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1/10, Jtr_pred = 0.083005, err = 0.025517, \u001b[31m   time: 4.232324 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.074746, err = 0.024600\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_regular_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type Linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 2/10, Jtr_pred = 0.049510, err = 0.014967, \u001b[31m   time: 4.004569 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.067373, err = 0.020600\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_regular_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type Linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 3/10, Jtr_pred = 0.032136, err = 0.009817, \u001b[31m   time: 3.895079 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.068393, err = 0.020700\n",
      "\u001b[0m\n",
      "it 4/10, Jtr_pred = 0.020423, err = 0.006067, \u001b[31m   time: 4.411544 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.066524, err = 0.020100\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_regular_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type Linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 5/10, Jtr_pred = 0.011957, err = 0.003100, \u001b[31m   time: 3.796672 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.063032, err = 0.018600\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_regular_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type Linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 6/10, Jtr_pred = 0.007625, err = 0.001917, \u001b[31m   time: 4.173824 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.061786, err = 0.017600\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_regular_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type Linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 7/10, Jtr_pred = 0.003913, err = 0.000717, \u001b[31m   time: 4.272190 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.062205, err = 0.017200\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_regular_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type Linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 8/10, Jtr_pred = 0.002451, err = 0.000417, \u001b[31m   time: 4.181925 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.058446, err = 0.015200\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_regular_NN_MNIST/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type Linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 9/10, Jtr_pred = 0.001193, err = 0.000100, \u001b[31m   time: 4.031349 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.057291, err = 0.015800\n",
      "\u001b[0m\n",
      "\u001b[31m   average time: 5.148870 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m\n",
      "RESULTS:\u001b[0m\n",
      "  cost_dev: 0.057291 (cost_train 0.001193)\n",
      "  err_dev: 0.015200\n",
      "  nb_parameters: 2395210 (2.28MB)\n",
      "  time_per_it: 5.148870s\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_regular_NN_MNIST'\n",
    "results_dir = 'results_regular_NN_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 10 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "weight_decay = 0\n",
    "########################################################################################\n",
    "net = Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size, weight_decay=weight_decay)\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "pred_cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost_pred, err = net.fit(x, y)\n",
    "\n",
    "        err_train[i] += err\n",
    "        pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    pred_cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, pred_cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "\n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "#             cprint('b', 'best test error')\n",
    "            net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(pred_cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " \n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xu8Ob5GSXODSjEZo/H/btG4AnI0i2iKQ/ihU7sy2zw9d+yckux9zJk+5ITPSjHnEGxxeDtWnTc/Tp4/bIypVzBfdS3nufly7tfjbDKIwErbHxRkRqAZcCWSbi9LDT05ZqbAKdGy3QuZwu+NvfmPnss7Rv357q27YxassWyo4axajdu/l0yRLg7HJu1ax5jGPHtrBnzx6GDBnM99+7XE4vvTSRDRvieOmliYgEJpeTqvDGGzP55JMvePfdD1AN4847B1C2bBWeeWYcqqE0afJ3brutFw8/PJjk5DDOO688AwcO5qWXXmX37kMkJ4fSo0dvVq/+lXXrfkc1lKZN/45qKD/8sAbV4lSqdD4VKtRh1apfSE4O48SJCnzxBcTHJ5CUFJ7ZV9jzXVSKFTtC8eJHqFixOHXrlmHNmmUUL36YihWLc+edNzF9+vOcOLGL4sUP89ZbE3nzzfF8993HFCt2ynKjFYDfJ8uNljVSwLO35IiInAcsBUap6twM1z4BnlbVbz3nXwKPqurqjOPExsbqjz/+eM5yxMbGkpv7CwSrVsEjj8CyZW4T5qGH4L77oEyZsxqmUOjCj3jr4+RJOHgQDhxIe/Xl/cGD2dc9iojIeeZUqpQrdXHeeelfS5aEqKj880C070cahUEXIrJaVWNz6hfUMxsRCQXmAO9kNDQedgI1vM6jgT/yQ7agpFkzWLoUvv3WORX85z+urMHvv9v6jp8oUQKqVHHH2ZCcDEeO+G6gNm92/zscPEg6d/fsiIxMb4AyM0rZtWW8HhZ29voxCi9Ba2w8nmavAXGqOi6LbguA+0VkFs4x4HCR3q/xlZYt4ZNPXIToli3O0CQlwciR0Levc0kz8pVixdwEs0wZqF377O5NSHBG58gR+OsvOHrUHSnvM2tLeb93r8v16t2WbWVZL8LCzjRA550Hmza9QJcubmswLMy9phze59ldO9vzjNfCw6F40P71C06CWd1XAD2BtZ4CagBDgfMBVPVl4FOc2/MmnOtznwDIGbw0buwOgJ9/drOd0aNd9Ohjjzn3M6PAEx7uMoZXrZr7sVSdF2JOxio7A3b6dAU2b3a+KKdOudeUw/s8KSn38mZHpUpQowZER6c/UtqqV3e6M/xD0Bobzz5Mts6nHi+0+/JHokJObKyb5Tz/PLzyCrz5pktD/corzu/ZKBKIuOW2yEgXwnUuxMb29GmfIjnZeRBmZ5B8Oc+s7dgx+OMPV8Zjyxa3TXnw4JkyVKhwphHKeERGnpseihpBa2yMABAdDS+8AEOHwoQJ8Nlnac4Du3ZBtWoWfGL4jZQ4qfza+zl2zH2Nd+6EHTvcq/exfDns33/mfeXKZT4z8j7OOy9/fobkZLdseuJE2mvGI7P2SpWgV6+8lc2MjXH2VKwITz7p9nBE3A5048ZwwQXOEAW5h6NRNImKcmmaYmKy7nPiRPYGadUqt8+VkTJlMp8VHTzYhpkzczYGvrafPHluP3uzZmZsjIJMyiwmJAT++19XVrRjR94LD4fx46FnT1tiMwoVEREuAUe9eln3SUhIW6LLzCj98gv8+WdK7+e4/fYzxyhWzH1WZkd4uJuJZHUtu/uya89rzNgYuadECRgwAO6+G2bNIuGee+Df/4bmzaFFC7cYft556TITGEZhJTwc6tRxR1acOuUM0nXX9WDOnHfOMACF8VfFgicM/xEaCj17ckeDBrB+PXgijxk61Ln2DBwIa9YEVkbDKACEhblSHpGRG2nQwL2vXNkF3hZGQwNmbIy8okGDtGW2m26CK690ld7+9jdo2tQFixqGUWQwY2PkPe3auVoF8fEwcaIzQimur6rw+efOH9UwjEKLGRsj/yhfHu6/3xma8eNd24oVcO21zl/0kUfAk+DPMIzChRkbIzCkBE/ExsKHH7pCOi++6GoZxMa65F6GYRQa8t3YiEg9EXlFRP5PRJJEZImP95UWkekiclBEDovIOyJyhl+tiHQSkbUikiAi60XkNr//EIb/CA2FG2+EuXOde8748c5zLTraXZ871+Vp83cxGsMw8pVAzGwa4vKV/eY5fOU9oA1wF9AbaAbM9+4gIi1xWaC/xlXp/ASYKSLX5FZoIx+oWBEefNCVAS1RwrWNHQs33OCW2QYNgl9/DaiIhmGcG4EwNh+pag1VvRXw6S+HiLQArgXuUNU5qjoP+CfQUkSu9ur6BLBMVf+lql+r6iBgETDMzz+DkV8sXQrz5rmYnfHjoVEjePjhQEtlGMZZku/GRlV9TFCejg7AblVd5jXOD8BWzzVEpATwD2B2hntnAS1EpPS5SWwElLAw6NzZ1ZvetQvGjYPrr3fXtm2D226DRYvyPkWwYRi5IlgcBC4ENmTSHue5BlAXCM2kXxzu58wm45ERFFSq5DITXHWVO9+wAb78Ejp0gPPPh8GDXZthGAWOYDE2ZYFDmbQf9FzD6zVjv4MZrhuFhfbt3WxnzhwXKPrcc3DJJWm54i0hqGEUGIIpN1pmfzmqAZeKyCZgccZ+IlITeNNzOlZE1qvqTq/r/YB+AOHh4cTG5lhGO0vi4uJydX9hIlC6KNewIRcfO8bSdu0AGLdpE3tDQ3mvUiW25EemwSyw70Z6TB9pFCldqGrADuADYIkP/WYDX2doCwGOAV8BYbjlMwVae/V5Hxjhab8XeCurz2jatKnmhtzeX5goELo4fVr1zjtVw8NVQbVtW9X581UTE/NdlAKhjwKE6SONwqAL4Ef14e99sCyjbSBtbyaFy3CVOn9W1VPA20BShn4XAQeAZOBdoFPei2oUCIoXh2nTXH73p5+GjRudo8GrrwZaMsMokgTLMtpC4AkRaamuHDRAKyAC6CoinYDVwB/ArcArnj6/A6OBE8D/ASVFpLyqptbbE5GOQMeKFSumTmcfeOABGjRowIABA9wHtWrF2LFjudyTxTgqKoqlS5fSu3dv1nnSqyQkJDBhwgTefNOt2g0aNIiqVasycOBAANq1a8fjjz9OmzZtAKhQoQKLFi2iW7dubNq0CYB58+Yxc+ZMZs92DnXDhg0jPDycoUOHAtCxY0cGDBhAhw4dAIiOjmb+/Pl07tyZnTvd6uDChQuZPHkyH330EQCjR48mISGBkSNHAtC1a1e6d+9Oly5dAKhXrx6zZs2iffv27Nu3D4AlS5YwatQoFi92K5Pjxo0jPj6esWPHAtCrVy86dOhA9+7dAWjUqBEzZsygdevWHDt2jLi4OBITExk0aBDffPMNAJMnTyYuLo6JEycC0K9fP1q0aEGfPn0AaNasGVOmTKF58+YkJSUREhLCypUruffee1m1ahUA06dPZ/ny5UydOvXsntOGDcRVqkSbsDD6XXYZn0yYwO7x47n06FGiBg8mqlmzPH1OKfcVtOcEsGLFinx/Tjt27CAxMTHb36eZM2eycOHCQv/7FBcXx4QJEwrkc/Ll797MmTPxGV+mP/48gEjgFs+xHBdrk3Ie6emzCXgtw32LgC3ATUBnYBeQCNTBLaNtBz73tI3HBYCuwy2hbQLe8lwrnZlctozmP4JCF6NGqZYo4ZbYrr5adcGCPFtiCwp95COmjzQKgy4owMtolXB7Ke8Dl+OWulLOK3n6FMftyXjTDVgKvI7b9N8JHFbVLeqW0dbjlstuAa4GPgOqAzNVtR4wHUhS1cN596MZQcPQoa5s4ujRzl36xhvhllsCLZVhFFoCEdS5TVUli2Obp08tVe2d4b5DqtpHVcuoaingeSBURGqLSBie/RlVna+qjVS1BNASuFhEduJS17yJYaRQoQIMGQJbt8L770P//q790CF44AFXAM4wDL8QLHs2maG43GobcY4CccB+ERmJm9YtwHmx1cctnyUCfUWkv3plMbA9myKwZ+PLXsAzz7jntHQpwy67jGKTJlHipZf4qXx5mkyfTve33uL3LVvO6TnZno3t2dieDYF1fc7NAVwBHCdtzyYeeCFDn1+BGp73DwBHgEqZjWd7Nv6jUOhizx63r1O9utvXqVtX9fDhcxqqUOjDj5g+0igMuqAA79n4C0/N4UyDPVPYDrT1vO8LnAb25qVQRiGhYkW3r7N1K8yeDZ06uQLx4Epax8UFVj7DCDKC2dhUAZbhHAHigG9xezgjReRGT5+HgbtFZD3QCOjhscSG4RuhoXDrrfD88+786FFXBuGii+Caa+DjjyH5XHLLGkbRIpj3bATYqartAUSkJ3CZqqaWE1DV9cAVIvIYEK2qi84YxPZsbM/mLJ/Th8uWsXnIEOp/9RWVFy/mWJUq/DpoEAPefTfT52R7NrZnY3s2IMH6j76nxs1EoCTOTXoTsFRVn87QryvwBm5PZ4Wq3p7ZeLGxsfrjjz+eszyxsbHk5v7CRJHRxenTrtbOSy/B22+7zNNr1riyCBemJbIoMvrwEdNHGoVBFyKyWlVzTPCW4zKaiJQQkR4icoF/RPMbq4G/AXd7XlvjgjhT8cg8ArdPUxd4KH9FNAo1oaHQtSssW+YMDbh9ngYN4NprXTlrW2IzDMAHY6OqJ4FpuAzLBYmmwBqcbGtw+zeNMuzZ3I2b8bzrcZzYExhRjSLD66/Dk0/CunWunHVMDNft35/zfYZRyPF1z2YtrvjY0jyU5WypjkvCeRek7tk0V9X7vfrE4JJ4thKRFcCIjPs2tmdjezZ58pxWrWJcy5b0iI8n2TO7KWjPCWzPJtC/T0Vpz+ZsYlp+B24AivtyT14fuISb07zOewITM/T5GJiHq+BZG5fipkxm41mcjf8wXXiRnKzNmjRx7999V/Xuu1X37g2sTAHGvh9pFAZd4Oc4m/m4ZbQPgQQR2Ssie7wP382b39gJNBaRjZ7iaT1wWZ+9icLF2azCGZ2jQEHbezIKMyIkiyckbNs2t8xWvz68/DIkJQVUNMPIT3xdRptE9sGTgSDFQaCt5/0+nJze/ARUUdXGIlIB+BmXOdow8p8hQ1zCz/vvh3vvdfV2Xn4ZikqlRqNI45OxUdUReSzHueDtIBBCmoNAM9Jyo60FrvIEdSYBg9Srlo1h5DsNG8JXX8F778HDD8P27WZsjCLBWQV1erIrXwyUw1XAXKsuvX8g8MVBAFymgb24pJ3f5a+IhpEJItCtG3TsCJGRru3FF50rdf/+EJKxuoZhBD8+GxsReRQYApQiLS/ZYREZrapj80K4nETKpC3jUt9HuHo2J0XkHlxw51XpBjFvNPNGy+PnlGMGAVUWhoZSceVKNjzyCM+efz4N+vY1bzTzRiuS3mgP4ZahJgFX4tL2X+k5TwT+5cs4/jyAFsBnXudDgCHZ9A/BFVvL9Lp5o/kP00V6fNJHcrLqrFmq1aq5LNO9e6vu3p33wgUA+36kURh0gZ+90e4DnlHV+1R1mapu9LzeBzwL/Mt38+Y3VgEXeBVP6wYs8O4gIlW9Tm/EJew0jIKHCNx2m6saOmgQzJzp9nMMo5Dgq7GpAXydxbUlQLRfpDkLVDURV+p5I3AM+FNVf82QQeBfIvKriGwD5gKBWO4zDN8pWRLGjEnvOPDMM/D994GVyzByia/GZjtwTRbX2nmu5ysiEgL0Bi7ExdNUEZGLVHWYOk80VHUIcLlHvpXA//JbTsM4JypVcq9Hj8KUKXDFFdC7N+zeHVCxDONc8dXYTAAeEZFpItJeRC4VkWtFZBowEBifdyJmyWXAJlXdos4jbhbQKZN+TwJjgIT8FM4w/ELJkrB+PQweDO++6wJCJ0yAxMRAS2YYZ4VPxkZVXwL6A+2BT4EfgYWe83tUdXKeSZg11YEdXuc7PW2piMiluLLQH+enYIbhV6Ki4OmnYe1aaN7cBYfaDMcIMnx2fVbVVz0zmWigKq4+zE6PN0IgyNb1WUSKAS/gltqyHsRcn831OdCuz2fznICI2rXZ0akTS77+mm9uvJHxBw6wPzTUXJ+D8PfJXJ/TuwyH4wIi2/vi3pZfBzm4PgOlcSlstnmOBFzutNjMxjPXZ/9hukhPnulj7VrVsDDVUqVUx49XPX06bz7Hz9j3I43CoAv85fqsqglAGaCgVYHK1vVZVQ+ragVVraWqtYAVwI2qGtxl8QwjhUaNXN2cFi3goYegSRPw/OdrGAUNXx0E3gH65KUgZ4s61+f7gc9w8TOz9UzXZ8Mo3FxwASxcCHPnwuFDd2zHAAAgAElEQVTDcPvtcCpQGaQMI2t83bPZDnQVkR9xDgK7SZ8aRlV1ir+FywlV/dQjj3fbsCz6tskPmQwj3xGBLl1cKepNmyAsDE6ehDffdO7SoaGBltAwfDY2z3teqwJNMrmuQL4bGxFpD7yIS0UzTVWfyXD9Hlz2gyTgL6Cfqq7PbzkNI1+IjIRLLnHv582Dfv1cgs9Jk6B168DKZhR5fHV9LpbDke9paj1BnZOADsBFQHcRuShDt3dV9WJVbYyLtRmXz2IaRmC47TaYPx/++gvatHHLa1u3BloqowiTo7ERkXAReVVELs8Pgc6CHIM6VfWI12kUBa8AnGHkDSLQqZMLCB02zO3p9O0baKmMIkyOy2iqmiAi3XBOAgWJzII6m2fsJCL34bIchJGhvIDnusXZWJxN8MTZnMtz+uQTKsXE0KRUKZ4CurRoweg1a5hboQKPrVnDoBEjLM4Gi7MJeJyNc6PmQ+C/vvTNrwO4FbdPk3LeE5iYTf/bgTeyum5xNv7DdJGeAqePn35SvfRSVVAtW1b1kUdUt27Nt48vcPoIIIVBF/i5xMAkoI+IPCciV4lIQxG5yPvw3bz5jZ24bNQpROOCNrNiFtA5TyUyjGDg0kth9WoXk9OuHbzwAsTEwN69gZbMKMT46o22yPM60HN4732I5zy/nQRSgzqBXbigztu9O4jIBar6u+f0euB3DMNwezotW7pj50746iuoWNFd+/e/oUED6NHD5WUzDD/gq7H5R55KcQ6oaqKIpAR1hgCvqyeoEzetWwDcLyJXA6eBg8AdgZPYMAoo0dHQq5d7n5DgZjzjx8Njj8Fdd8GAAVC7dmBlNIIen4yNqi7Na0HOBc0hqFNVH8x3oQwjmAkPh1Wr4LvvYOJEt8T2/PMwaxZ07Rpo6YwgxueszwAi0gGIxe2VPKWq20XkSpwLcnb7JYZhBAsZl9imTIF/eBY3Pv8ctm2Df/7TBZEaho/45CAgIpVFZCXwEW4p6k6ggudyH+CJvBHPMIyAEh0No0al7efMng39+7v2QYOc4TEMH/DVG20icB6uBPOFpK8l8wXQ1s9yGYZREHn11fRebHXrwsMPB1oqIwjw1di0B/6jqps4Mwr/jAqZ+YWnRPVGEdkkIoMzuT5QRNaLyBoR+VJEagZCTsMoNKQssb33npvVDB4MjRu7a8eOOWPkCUQ0DG98NTbgkllmRgXghB9kOSt8zI32M65Y2iXAB7j8aIZh+IOUJbaePd35ggUu+WeNGrbEZpyBr8bmG+ABzx/4FFJmOH2Br/wqlW/4khvta1U97jldgQv8NAwjL+jW7cwlti5d4PjxnO81Cj2+eqM9BnwLrAPm4QzN3SLSCGgEBCJJp0+50by4E1iYpxIZRlEmMy+2NWvSvNaWLoVmzQIroxEwfI2zWSciTYERQG/cktpNwJfAnV5R+vmJZNKWaVZnEfknzmX7jKIelojTEnEW+kScgUrw+Nln7jm98QaNqlWj4bXXcrJYMRqdd54l4rREnMFzAC2Az7zOhwBDMul3Na5sdKXsxrNEnP7DdJEe04eqJierfvON6mWXaRKovvhioCUqEBSG7wZ+TsRZEEnNjSYiYbjcaAu8O4jIpcArwI2quicAMhqGAWlLbF9/zZIyZeDBB2HgQFArMVVUOKsMAgUJ9S032lhcfND7IgKwXVVvDJjQhlHUiYxkcJ06/NCyJZQt64yQUSQIWmMDPuVGuzrfhTIMI1uSReDFF9NmNatXQ82aUKFC9jcaQU0wL6MZhhHMiLgs0zfeCH//O2zeHGiJjDzEjI1hGIEjPNxlI9i/H1q0gB9+CLRERh5xTsZGRBqJyH0icr+IXOxvoQzDKEK0bAnffw/nnQdt2rhMBEah46yNjYjcCywD2gDXAT+IyAA/y2UYRlGifn1YvhwaNYJ33gm0NEYekKWDgIhEalqqF28eA1qo6kZPv77Ak8DkvBHRMIwiQeXK8PXXUMzzP/D+/c5jrZit9hcGsnuKv4lIj0zaBUj2OjdHecMw/ENUFEREuHxqrVvD7bc7JwIj6MnO2NwODBSR5SLindBoDLBCRGaLyMe4Gc0zeSmkYRhFjIgIuOMO5zxw7bVw4ECgJTJySZbGRlWX4fKJvQ4sEJE3RaSqqk4CrsIl5vwct6Q2MV+kNQyjaCDiyhTMnAkrVsAVV1jJgiAn28VQT+qbV4H6wG5grYgMBTao6gTP8Ut+CGoYRhGkWzf4/HP480+4555AS2PkAp923lT1iKoOwpUSaA5sEJFb8lQywzAMcHs3338Pr73mzi2fWlCSpbERkUgReUpEVorIzyIyFUhQ1U7A3cBwEVkqIn87mw8UkYs8JZqPi8gfIjIyQ1G2zO4JE5GxIvKNiJwQkSy/bSLSSUTWikiCpyT0bWcjn2EYBZAGDaB6dUhKgltvBU9afCN4yG5m8xrQEXgeeAKoAiwWEVHVL4DGwPueNp+evIiUBb7AebB1AkYCDwP/zeHWSOAu4DjwfTbjtwTmAF/jykV/AswUkWt8kc8wjALOyZNw4gT07w9Dh9osJ4jILhFnB+BWVV0MICLfAfuBurhyzEnASyLyDq6omi/cA0QAN6nqEZyhKgWMEJExnrYzUNVDIlJOVdWT6fmqLMZ/Alimqv/ynH8tIg2BYThnBsMwgpnISPjwQxgwAJ5+GrZvh9dfh7CwQEtm5EB2M5sNQE8RKScikUB/4Biu/HIqqnpQVR/08fM64AqeeRuVWTgDdEYVzQyfk+2/MCJSAvgHMDvDpVlACxEp7aOMhmEUZIoXh1degVGjXLaBnj0DLZHhA9nNbO4AZgD7cMteW3EzndxEWF0IfOXdoKrbReS459pHuRi7LhCKM5LexOGMagyu4JphGMGOiFtGq1kT6tULtDSGD2RpbDzpaFqISBQQpqoH/fB5ZYFDmbQf9Fw7K0SkPfAirnjaYk/zIa/rVwJTPKc3ksHYiEg/oB9AeHg4sbGxZytCKnFxcbm6vzBhukiP6SM9eaWPHrt3s6pkSX6LjPT72HlFUfpu5Fg8TVWP4ZbP/EVmy2GSRXuWeDzYJgHtcEt7v2Yy/nZcLrePMhtfVacCUwFiY2P1xx9/PBsR0hEbG0tu7i9MmC7SY/pIT57o4/BhuPhi2LEDPvjAZR0IAgrDd0N8rLaa3xnuDgJlMmkvTeYznuy4DOeosEVVTwEfe9pTx1fVbbhgVPCvwTQMoyBRurTLGl23Llx/vXMaMAoU+W1sNuD2ZlIRkRpAFGfuteREdWCH1/kaXILQCzP0uxA3q4k/y/ENwwgmqleHb76Bq6+GO++EJ58MtESGFzkuo/mZhcAgESmpqkc9bbcBJ4ClZzmWANEishG3Z7Mat5x2K/AKpO7ZvOTpe0mmg4h0BDpWrFgxde30gQceoEGDBgwY4Mr0tGrVirFjx3L55ZcDEBUVxdKlS+nduzfr1q0DICEhgQkTJvDmm28CMGjQIKpWrcrAgQMBaNeuHY8//jht2rQBoEKFCixatIhu3bqxadMmAObNm8fMmTOZPds51A0bNozw8HCGDh0KQMeOHRkwYAAdOnQAIDo6mvnz59O5c2d27nROggsXLmTy5Ml89JHztRg9ejQJCQmMHDkSgK5du9K9e3e6dOkCQL169Zg1axbt27dn3759ACxZsoRRo0axeLHbBhs3bhzx8fGMHTsWgF69etGhQwe6d+8OQKNGjZgxYwatW7fm2LFjxMXFkZiYyKBBg/jmm28AmDx5MnFxcUyc6NLo9evXjxYtWtCnTx8AmjVrxpQpU2jevDlJSUmEhISwcuVK7r33Xlatcltt06dPZ/ny5Uz1BPSdy3OaOXMmCxcuzNfnlHJfQXtOACtWrMj357Rjxw4SExPz5jndfTdb9+5lSPnytIqK4rUxYwr071NcXBwTJkwokM/J198nn1HVfDtwTgDxuM38q3Gb838BT2Xotwl4LUNbB+AWYBpupvI4cBJoCYR5xn0PSATG44q7vYyb7fwBPJedbE2bNtXckNv7CxOmi/SYPtKT7/r46ivVQ4fy9zN9pDB8N4Af1Ye///m6jKbOo60tbibyES5zwAvA8Axdi3v6eDMFl7HgTs/5Uzgjc7lXnz9wBulq4DOcwbkdl3nAMIyixoED0KkTXHkl7NyZc38jzxAN0nQPnkSgdwF1cIbpJ5wzwAGcpV3gqcMzD6gMJADbVbVhhnFSltHuPv/884FzX0br16+fLaN5ltEOHz5cYKf9gVhGO3ToUIF7ThC4ZbRdu3bl23P637RpPLt5MyFly/LzU0/xr2nTMn1OgVpGe/rppwvkc/L19ykmJma1qubovx3MxuZW3CynNml7NntU9QGvPlfiltQaA+NU9ZGsxjPXZ/9hukiP6SM9AdHHmjVw3XVw9CjMmeOcCAoAheG7ISI+GZtgLu79B3Albi/nIs/7xAx9tgO9gS35KplhGAWLSy5xRdhq1oTPPgu0NEWS/PZG8ycpkURZTs3Uxdn4HHRkGEYhJjoavvsOoqLc+aefQkgIXHONS39j5CnBbGyqAMtwjgAhuDLVoSIyksz3bPqLSIds9mzM9dlcn831Odhdn8/iOb2/fz+1t23j18hIXq9alS0NGzLvww/N9bkwuD7788DF0ywCNuJcpd8DJmbocyXOcSAZc33ON0wX6TF9pKfA6OPkSdVXX1WtU0cVVBs1Ul20KF9FKDC6yAUURNdnP2N7NoZhnDthYXDXXbBxI7z9tqsCesRT/eTECTh9OrDyFTKC2dj4tGejqmvySR7DMIKR4sWhRw9Ytw5uvtm1PfccXHABTJkCCbmpqmKkEMzGxnvPJg6vPRsRuRFARJqJyE6gJm7P5tcsRzMMo2hTrJg7AFq0gKpVXUXQOnVg3Dg4Zrl8c0MwOwgIsFNV2wOISE/gMlUdltJBVVfh8qfNAD5W1Q/OGMQcBMxBwBwEiqSDQI6/T6p8MWcOBx9+mNoPP8znTz0Fs2aZg8A5OggEc1BnC2CEql7rOR8CoKpPZ9J3BlkYmxQsqNN/mC7SY/pIT1Dq4/vv4bzzXLzOtm2uhMGDD0L58rkaNih1kYGiENS5CrhERLaIyCbgX8AC7w4icqWI/AT0In0ONcMwDN/5+9+doQEXFPrkky5AdNAg+PPPwMoWJASzsUmZkglezgLeeza4QmrVgSRsz8YwDH/Qvz+sXesSfI4bB7VqwcMPQ5CuEuUXwWxsLgPWqGptVa0LTAA6qeowVV0AoKoLVLUy8A7QRzMEdBqGYZwTjRrBO+84t+l//tO5SqdkIfjjj8DKVkAJZgeBjJU6dwLNz3YQcxAwBwFzEDAHAcjF79Pw4XTp3BliY2lftixPff01X1WqxJSyZdkaEWEOAin4EvlZEA9cBoFpXuc9yZBBwOvaDOCW7MazDAL+w3SRHtNHegq1Pv78U/WRR1SjolxWgptuUl29OsvuhUEX+JhBIJhnNjuBGl7n0bisAnnGkSNH2LNnD6cziSweM2YMcXFxefnxQUNh1UVoaCiVKlWiVKlSgRbFKKhUrgxjx8Jjj8GLL8KECfDVV7BrF0RGBlq6gBLMxibVGw2X+ywKV6EzFREpAbyJS2lzhYj8qJ5M0GfLkSNH2L17N9WrVyciIuKMTNKqSoMGDc5l6EJHYdSFqnLixAl27doFYAbHyJ4KFZzH2iOPwP/9nzM0yclwzz3QvTu0aVPkMk0Hs4OAL95oI4HrcVmhqwD/d64ftmfPHqpXr05kZKSVLCiCiAiRkZFUr16dPXv2BFocI1goXdqVpAYXn/Pxx3DVVXDFFa7EQRHyYAtmY5OjNxquQmc7VY0CygKn5RwtxenTp4mIiPCL4EbwEhERkekyqmHkSJ06sGULTJ7sPNauv55Xf/utyKTBCWZjk5k3WvWs+qhqInAYOOeQX5vRGPYdMHJFeDjcey/8/jtMmUKiiHObLgIE855NZr/1GeekOfbx1fX57rvvZv369QAUK1aMCy+8kK1bt3LC80VRVXbv3s3+/fsBqFKlCqGhoezY4exhqVKlqFq1Khs3bgSgePHixMTEsHnzZk6ePAk499YDBw5w4MABAKpVq4aIpO4TlClThooVK/L7778DEBYWRr169di0aROnTp0C4IILLmDv3r0cOnQIgOrVq6Oq/OHx/S9XrhzlypVLdQ8tUaIEdevW5bfffiMx0VVoqF+/PvHx8RzxpFuvUaMGp0+f5k9PpHT58uUpXbo0W7a4yg0RERHUrl2bDRs2kJycTEJCAqrKzp07OXr0KAA1a9bkxIkTqUtQFStWJCoqim3btgHOrbJmzZrExcWhqogIDRo04H//+1+q+2etWrU4duwYe/fuBaBSpUpERETwv//9D4CSJUsSHR2d6pyQ2XOqU6cOhw8fztVzSkpKYsyYMeb6bK7PuXtO06YRl5jI0+++S4f27el+++0F6jmZ63OaO3ML4DOv8yHAkAx9PgNaeN4XB/bhyQeX8cjJBXH9+vXZXv/111+zvV4QeO+993T69Ol5PnYw6CI35PRdyEhhcG/1J6aPNJo2baq6d6/qtdeqLl0aaHHOCYpA8bRVwAUiUltEwoBuZMiN5jm/w/P+FuArj3KKJLNnz2bGjBlBN7ZhFGpCQ91eTteuhTr7QNAaG3V7MPeTVs9mtqr+msEb7TWgvCdR50BgcGCkNXLL6dOnSUpKOqNdVUnIRXGrE0VkvdwowJQuDfPmwV9/wS23gGdJvLARtMYGQFU/VdUYVa2rqqM8bd650RJU9VZVraeql6lqkS0P3bt3b+bMmcPSpUsREUSEESNGpF7/8MMPiY2NJTw8nCpVqvDoo4+m87rauXMnXbt2Td0nqVu3Lk888USmYzds2DDd2BlJTk7mmWeeoV69epQoUYKYmBjeeOONdH3atGnDLbfcwtSpU6lbty7h4eH88ccfjBgxggoVKvDtt9/SrFkzwsPDef/99wHYunUrnTt3plSpUpQsWZKOHTumrt+nICKMGzeOhx56iIoVK3LxxRfnUrOG4QcaNnRlC5YvB8++VGEjmB0EEJH2wIu4OJppqvpMhuspQZ1Ngf3AbXqOQZ3BzhNPPMH27ds5dOgQkydPBtyGKLglsO7du9O/f39Gjx7N5s2bGTJkCMnJyTz33HOA22w+ceIEU6dOpUyZMmzZsoUNGzZkOvbWrVtp1apVlrI88MADvPHGGwwbNowmTZqwePFi+vbtS/ny5bnhhhtS+3333Xds3ryZZ599lsjISEqXLg3A8ePHueOOO3j00UeJiYmhWrVqnDx5krZt2xIaGsqrr75K8eLFGT58OK1bt2bt2rWUK1cuddyxY8dy5ZVX8tZbb5GcnOxfRRvGudK1K6xcCR984AJCy5YNtET+xZeNnYJ44AzMZqAOEIYL2LwoQ58BwMue992A97Iaryg4CNx8883aunXrdG3Jycl6/vnna+/evdO1v/baaxoeHq779u1TVdWoqChdsGCBT2Nnp4vff/9dRURnzJiRrr1nz54aGxubet66dWsNDw/X+Pj4dP2GDx+ugM6fPz9d+5QpUzQkJEQ3b96c2rZjxw4NDQ3V0aNHp7YB2rhx4yzl8wVzEMgdpo80ztDF6dOqnt+5YIEikBvtMmCTepbGRGQW0AlY79WnEzDC8/4D4CUREY+Ccs1DD8Evv7j3x4/XzNfUR40bw/jxuR/nt99+Y/v27XTt2jXV9RngqquuIiEhgXXr1tG6dWsaN27MkCFD2L9/P1dddRXnn3/+OX3el19+SbFixejSpUu6z2vbti0zZ85MddMEaNq0KVWqVDljDBFJdVdN4YcffqBJkybUqVMntS06OporrriCb7/9Nl3f66+//pxkN4w8p3hxV/3z9GkYPRoeeAC8ZuXBTDDv2eR7UGdhJCUW4LrrriM0NDT1qF27NkBq/Ml7771HbGws//73v6lZsyaNGzfmyy+/PKfPS0pKonTp0uk+r3fv3iQmJhIfH5/at3LlypmOUbZsWcLCwtK1xcfHZ9q/cuXKqXFLOY1rGAWG9eth1Cjo0QMycYwJRoJ5ZhPwoM4HH0wf1FmhQoV8DerctOnsgjqPHDnCqVOnOHXqVOrG+V9//QXAk08+SUxMDOACMPft28exY8eIjo7m6NGjRERE8Oijj/LII4+wfft2xo8fT8eOHfniiy+oWrUq4PZS1q9fn21Q58mTJylevDgff/wxUVFRqcGmERERVK1alf3793PkyBGOHz9OhQoVMg3qTE5OZv369emCOiMiIti6dSuqmi6oc/fu3YSHh6c+O4CjR4+mnltQpwV1BrI+VHb1bJ6rWZNHFi3i1Ro16LN9uwV1BurAgjrPmu7du2vz5s3TtSUlJWn16tV16NChZzXW999/r4Cu9tTq8B47O11s2LBBRUQ///zzbMdv3bq13nzzzWe0Dx8+XMuXL39G+8svv6whISG6ZcuW1LadO3dqWFjYGXs2EydOzP6HywHbs8kdpo80stVFcrJqnz6qoJrNfmmgoQjs2aQGdQK7cA4At2fokxLUuRwL6uTCCy/kww8/ZP78+URHR1OtWjWqVavG888/T8+ePTly5AgdOnQgLCyMLVu2MH/+fD744ANOnz7NtddeS69evYiJieHkyZM8//zzVKlSJbWUgPfYp06dokyZMlSrVu0MGerXr88999xDt27dePTRR4mNjSUhIYFff/2V3377jWnTpp3Tz9a7d2+effZZOnTowMiRIwkJCUl1k+7fv3+u9GYYAUEEJk1yJQr694d27VxutSAlaI2NqiaKSEpQZwjwunqCOnGWdgEuqPMtT1DnAZxBKrIMGDCAn3/+mb59+3Lw4EGGDx/OiBEjuO222yhVqhSjR4/m9ddfJyQkhDp16nDDDTcQFhZGSEgIF198MS+++CI7duwgMjKSyy+/nM8//zw1E3ZWY2fGpEmTiImJ4dVXX2XYsGGUKlWKiy66iDvvvPOcf7YSJUrwxRdfMHDgQO68805UlTZt2jB37tx0bs+GEVRERMDcuXD4cFAbGvAsKRkQGxurP/74Y5bX4+Lisi0Itn79ei666KK8EC3oKOy6yOm7kJHY2Fiy+24VNUwfaZy1Lr76Cv7xjwJVeE1EVqtqbE79gtkbzTAMo+jw6afQtq0rNR2EmLExDMMIBtq3h06dXKnpZcsCLc1ZY8bGMAwjGChWDN54w1X8DMIM0UFtbESkvYhsFJFNInJGRmcRKSEi73murxSRWvkvpWEYhp8oXdo5DPz1F9x6KwRRbr+g9UYTkRBgEtAOlz1glYgsUFXvdDV3AgdVtZ6IdAOeBW7Lf2kNwzD8RMOGboYj4mY7QULQGhsKQG40wzCMgHDzzWnvDx4MigzRwWMWz8RyoxmGUbSZPx9q1YKffw60JDkSzDObgOdG27o1fW603bt352tutLCws8uNBlCuXDnKlSuXmj+qRIkS1K1bl99++y01C3P9+vWJj4/nyJEjANSoUYPTp0/z559/AlC+fHlKly7Nli2uFl1ERAS1a9dmw4YNJCcnZ5sbbc+ePQBUrFiRqKgotm3bBri8SzVr1iQuLg5VRURo0KBBprnR9u7dC5AuNxpAyZIliY6OTpcbLeNzqlOnDocPH87Vc7LcaJYbLT9yo/nynN4eN463Tpwg8ppr2DlvHr0eeshvz8lyo1lutAJPYdeF5UbLHaaPNPyiixUrVMPCVNu3V01MzP14Zwk+5kYL5mW01NxoIhKGS0WzIEOflNxoYLnRDMMojDRv7gI9Fy2C//430NJkSdAaG3V7MCm50eKA2erJjSYiN3q6vQaU9+RGGwic4R5dlJg9ezYzZszw65hLlixBRFKn1YZhBIB+/eCuu1zxtQJKwZXMB1T1U+DTDG3DvN4nALfmt1wFldmzZ7Nv3z569+7ttzGbNGnC8uXLqVu3rt/GNAzjLBGBqVMLVM60jATtzMbIO06fPk2Sj9UBS5UqxeWXX56a/TkQJCQkZNqe4hRwLiQlJaU6XRhGUJBiaJYudTnUPIURCwpmbIoIvXv3Zs6cOSxduhQRQURSSwC0adOGW265halTp1K3bl3Cw8P5448/2LBhA926daNGjRpERkbSsGFDxo8fT7JX1HJmy2gNGzbkxRdfZOjQoVSsWJFKlSpx3333pXpzZce3335L69atiYyMpHz58tx9992pHm0AM2bMQET44YcfaNOmDREREYwdO5Zt27YhIrzzzjv06tWLMmXK0LFjR8AZjhEjRnD++edTokQJGjZsyLvvvnuGfmJjY5k/fz4NGzYkPDyclStX5kblhhEYTp2CJUvcsloB2qIO6mU0w3eeeOIJtm/fzqFDh5g8eTLgXD1T+O6779i8eTPPPvsskZGRlC5dmt9++4369evTo0cPSpYsyS+//MLw4cM5ceIEQ4YMyfbznn/+ea666irefvtt1qxZw5AhQ6hZsyaPPvpolvd89913tG3bls6dO/PBBx+wf/9+Bg8ezMGDB/nggw/S9e3evTv33nsvw4cPp0yZMqntjzzyCDfddBPvv/8+ISEhgHN5HTNmDMOHD6dZs2bMmTOHHj16ICKp7qYA27Zt49FHH2XYsGFUrlyZ2rVr+65gwygotGsHo0bBkCHOeeDf/w60RA5fXNaKwnHOrs+tW6u2bq1/NWuW+l4nTXLXjh1La/M+pk931/fuzfz6rFnu+vbtmV8/xxKxN998s7Zu3TqTH6G1hoeHa3x8fJb3Jicn6+nTp3XUqFFau3bt1Pavv/5aAV27dm1qG6CtWrVKd3+nTp3OKEmdkZYtW2qbNm3StX355Zfpxp8+fboCOn78+HT9tm7dqoB27tw5Xfv+/fs1MjJSR4wYka69Q4cOGhMTk3p+xx13KKA///xztjKqmutzbjF9pJFnukhOVu3SRTUkRHXJkrz5DA8UAddnw480bdqUKlWqpGtLSEhg+PDh1KtXjxIlShAaGsrjjz/O1q1bUwNAs+Kaa2qzyFUAABBASURBVK5Jd37RRRelBsplxvHjx1m+fDldu3YlMTEx9WjZsiWhoaGsXr06Xf/rr78+03Eytq9bt47jx49z663p/URuu+02fvvtt9QgU3ABsI0bN8725zKMoEAEZsyAevUgw5JxoLBltNyyZAkA/8usOmVkZOr1TKlQIfvrNWpkf92PVK5c+Yy2xx57jGnTpjF8+HCaNGlCmTJl+PDDD3nqqadISEjgvPPOy3I876UtcNkOstrIBzh48CBJSUkMGDAgNYLZm5QI/+zkzaw9Pj4+0/aU84MHD1KpUqVsxzSMoKRUKVf3pmLFQEsCmLExPEgmLpPvv/8+DzzwQLp9lk8++SRPPr9MmTKpTgvXXXfdGderVauW7jwzeTNrr1q1KgB79uyhfPm0tHi7d+8GXPqenMY0jKDF848UW7fCnDmu8FqAMGNThMhpdpGREydOUKJEidTzpKQkZs2alReiERUVxeWXX87GjRsZNmxYzjf4SKNGjYiMjOT9999PN+7s2bOJiYmhYgH5r88w8pTp0+HJJ90s5447cu6fB5ixKUJceOGFfPjhh8yfP5/o6GiqVat2xozBm3bt2jFp0iTq1atHuXLlmDRpkk/uy+fKmDFjaNu2LcWKFeOWW26hZMmSbN++nU8++YRRo0YRExNz1mOWK1eOhx56iKeeeorixYsTGxvL3Llz+fTTT88uiaBhBDPDhsG338I998All8Cll+a7COYgUIQYMGAA11xzDX379qVZs2ap2V6zYuLEibRq1Yr77ruPvn370qhRoxxdnnNDy5YtWbZsGXv37qVnz5507NiRMWPGUKNGjVztp4wcOZIhQ4YwZcoUbrjhBpYtW8bbb79Nt27d/Ci9YRRgiheHWbPcPvFNN4En63m+4ovLmj8P4CLgS+A48AcwEgjx4b7SwHTgIK4uzTtA+Qx92gEzgW24UgIjfJXLsj77j8KuC3N9zh2mjzTyXRcrV7oM0fff77ch8dH1OV+X0USk7P+3d/dBUlVnHse/P5HskA0YI0gIEDWBCIaNmzhiSEQQWQNJEYQyOFRWC3YjJda6iSRjDJgUEmE3oRxdMOMGE7BMlYCmAgHjYNBEAluI4GoqkUFkXZV3A+ElGEZgePLHuc3cufY4wzC370z386nq6plz7+0+fbp7njn3nHse4GlCNs2xwMeBewk9rLuaOXwpcDHwNeAkIcXzcmBobJ9RwKcIwcz/bXXOubjBg+HJJyHKUVNIhR6zuQXoAow3s8PAakndgJmSfhiVvYukIcAXgGFm9ruobCewQdJIM3s62rXSzL4ZbR+b9otxzrkO55prwv2RI/Dyy2GVgQIo9JjNaELCs3hQWUIIQMOaOW5vLtAAmNnzwP9H23JlJ/Mc65xzLmnqVLj2Wti6tSBPV+hgMwDYEi8wszcJ4zcDTue4SG0zxznnnMvnnnugc+cwYaAAK0QXOticCxzMU34g2na6x5UBIyRtk5QvMVonSUuj7RskXXi6FY4LY2GulPlnwBWNCy4IM9Rqa2FFMslx28viOpt831Y1Ud7kcZI6AYOBl4HPARslrTCzzbHdPgNsN7N+kioIkwpuiD3GFGAKQFlZGeXl5U0++fTp06mvr+ess/LH57q6OjZv3px3W6kp5rY4efIkW7du5cYbb2zxMbW1te/52So13h4N2kNbfHTgQN6sqoKqqlSfp9DB5gDwwTzl55C/5xI/Lnmp92CgjjCWc0zSEsIMt/hfuYuBe6Kffw48IEnRdD3MbAGwAKC8vNw2bdrUZAUOHz7M3r176d27N126dHnX0iab862NVqKKsS3MjKNHj7Jz505GjhzJ+PHjW3xseXk57/XZKjXeHg2KoS1ausxToYPNFhJjLJL6An9P/jGZ+HFDE2W9gb+LHbcDSE6r6AZsBzCzE5IOAecB+0634t26dQNg165dHD9+/F3b9+zZ42trRYq1LTp37kzPnj1PfRaccy1X6GBTA1RK6mpmufSLNwBHgTXNHPddSVea2bqo7ONAV2BCNM35BeCtxHECqiVdAuwnvN7k6bgxwJgePXqc6s7edtttDBw48NTqw0OHDmXu3LmMGDECCOt4rVmzhkmTJp3KUFlXV8eUKVN45JFHAKisrKRXr15MmzYNCEu/zJgxg+HDhwPQvXt3Vq1aRUVFBdu2bQNg2bJlLF68mMceewwISb/KysqYPn06AGPGjOHWW29l9OgwAa9Pnz4sX76c66677tTy/TU1NVRXV7Ny5UoA5syZQ11dHbNmzQJgwoQJTJw4kXHjxgHQr18/lixZwqhRo9i3L8TgZ599ltmzZ7N69WoAqqqq2L17N3PnzgXgpptuYvTo0acSjw0aNIiHH36YYcOG8fbbb1NbW8uhQ4eorKxk7dq1AFRXV1NbW8v8+fMBmDJlCkOGDGHy5MkAXH755Tz44INcccUV1NfX06lTJzZs2MDUqVPZuHEjAIsWLWL9+vWnVj5o6n36bHQNQb73afHixdTU1BT0fcod197eJ4Dnnnuu4O/T9u3bOXHiRLt7n7L4PtXW1jJv3rx2+T619PvUUirkgGd0Uedm4I+E8ZOPAVXA/WZ2V2y/bcAaM/vXWNkq4BPAtwgXdf4X0AfoT+jVvAH8ClgVHbKQEFi2ADMJkwwWAWWW50U3dxqtOcXQHW4r3haNeXs05u3RoBjaQtILZtbswFNBezZmdkDSNcADwErCOM19hGCQrFenRFlFtO9Cwiy6DcD5NO6p9AUeTxw3GHgQmH7mr8A551xrFHw2WjRbbEQz+1yYp+wgMDm6Iel6oB54ihCY1gF7CUFok5mtkPQy4cLPAcA3gD20cszGOedc63XkFAMCdpjZKABJNwKDzSyZDOUWM9sR7fN/nMGYzXudu6yrq2PevHlFf44ZWjZmc+LEiXZ7jtnHbHzMJt/75GM2RTRm05ai9dJmmtkXot+/A2Bm/xHb56lon/WSzib0bHr4mE26vC0a8/ZozNujQTG0RUvHbDpyPpuNQH9JF0l6H2FMJ3kZ7Aogl5bueuA3+QKNc865dHXYng2ApC8C9xPGbBaa2WxJs2gYsykDfgZ8GvgzUGFmrzXxWH8izGhrre74WFCOt0Vj3h6NeXs0KIa2uMDMms2v3qGDTXsiaVNLupKlwNuiMW+Pxrw9GpRSW3Tk02jOOec6CA82zjnnUufBpu0syLoC7Yi3RWPeHo15ezQombbwMRvnnHOp856Nc8651HmwOQOSLpH0jKS/StolaVaU1K3kSPqKpBWSdko6IukFSROzrld7IKl31CYm6QNZ1ycLks6WdKekVyW9I2mHpPuyrlcWJFVI+t/oM7FT0iOSPpJ1vdLWkZeryVS0gvXThFWsxxJSHtxLCOB3vcehxWoaYR262wnXDXwReFRSdzObn2nNsjcXOELI21SqFgHXAHcTVmLvCxRXhr0WkPRlYDHwI6AS6EVI8PiEpHIzO5ll/dLkYzatFC2PcwfhgqbDUdkdhBWsP5wrKxVRUNmXKHsUGGJmF2VUrcxJGgr8EphDCDpdzexItrUqLEmjCKu8X5pI215yoozC/c3ssljZlwmfkUvMrDazyqXMT6O13mjgqURQWQJ0AYZlU6XsJANN5EVCGoiSFJ1SnQ/MouNfJX4m/oWwVFRJB5pIZ+BQouxgdF986W1jPNi03gASqazN7E3gryRSX5ewzxFOM5aqW4AywimTUnYFsFXSA5IOR2OcvyiFcYo8FgJDJd0kqZukTxBOo/222IOxB5vWO5eG/0jiDkTbSlqUJG8sJfqHVtJ5wPeBaWZ2POv6ZOzDwCTgHwkL5k4GLgOWSSrq/+aTzOxXhLZYQOjhvEJY23F8htUqCJ8gcGbyDXipifKSIelC4FHgl2b2cKaVyc5sYIOZPZl1RdoBRbexZrYfQNJuYA0hkeIzGdatoCRdDfw3Ia19DdCTMM67TNJIM6vPsHqp8mDTegeAD+YpP4f8PZ6SIOlDhC/Rm8A/Z1ydTEj6JGGc4ipJuc/I+6P7cyTVm9nRbGqXiQPAa7lAE1kHHCPMSCuZYEOYsbrCzL6dK5D0EuGU/FjgF1lVLG1+Gq31tpAYm5HUlzC9dUveI4qcpPcDTwDvA75kZm9nXKWs9CcMBK8n/KE9QMPpxB2ESQOlpKkZVgKKdqpvEwYAL8ULzOwV4Cjh8omi5T2b1qsBKiV1NbO/RGU3ED40a7KrVjaiTKiPE/7Qft7M3sq4SllaB1ydKBsFfJtw/VHenEpF7Ang7sT0+KsIAfn32VUrE28An4kXSBpImMX6ehYVKhS/zqaVoos6NwN/BH4AfAyoAu43s5K7qFPSAuBm4OvA84nNL5rZO4WvVfshaRLhwsZSvM6mG+F7spNwvVFXwndmi5n9U5Z1KzRJXwfui265MZvvEc4GDCrmswEebM6ApEuAB4AhhHGanwAzi3mQrymSXgcuaGLzRWb2euFq0/6UcrABkNQPmEe4Bu0Y4SLG283sQKYVK7Bo9t0twFTCabODhJ7wd5rKIlwsPNg455xLnU8QcM45lzoPNs4551LnwcY551zqPNg455xLnQcb55xzqfNg45xzLnUebJwrYpKGR+moB2VdF1faPNg455xLnQcb55xzqfNg41wKJF0paU2UlXK/pIckdY22TYpObV0uaa2ko5K2ShqX53H+TdKrkt6RtE3S7Xn2+ZSklZIOSjoi6XlJyTXHukt6PNr+mqRbU3rpzuXlwca5Nibp84QcLXuA64FvEFZ7XpTYdSlhjbDxwB+AxyVdGnucmwnpCFYAYwirat8r6c7YPgOA/wF6EdbcGgcsA/omnushwgrL44BngR9JGnzmr9a5lvG10ZxrY5LWAifM7OpYWS4j5T8A5YTAM8PM5kTbzyKsIv6SmVVEv28Hfm1mk2OPUw18FehpZnWSFgNDgf75ErJJGg78Fvi+mX0vKusM7AJ+amZ3Jo9xLg3es3GuDUUJ5IYAj0k6O3cjrOx7HLgstvuy3A9mdpLQy8n1NvoAHyH0ZuKWAt0IQQtCWuWlLcj8+evYcx0HXo2ew7mC8GDjXNs6F+gEVBOCS+72DiFZWPz0VjLB3FuE02HE7vcm9sn9/qHo/jxgdwvqlUxVfgwoa8FxzrUJz9TpXNs6CBgwE3gyz/ZdwLXRz+cD+2PbzqchcOyOlcX1jO7/HN3vpyEwOdduec/GuTYUZVp8DrjYzDblue2K7X5q9lk0RjOWhiynOwiB6SuJp5gAHCZMKIAwDjRBkvdSXLvmPRvn2t4dwDOSTgI/B/4CfBT4EjAjtt/XJB0jpEy+GegHTIQwhiNpJvBjSfuB1YQsl1OB6WZWFz3G3cBG4HeS7iX0dD4N7Dezham+SudOg/dsnGtjZrYOuAroAfwMWEkIQNtpPAZTQejdLAcuBW4wsxdjj/MQ8O/RPk8QAtE3zew/Y/u8AlwJ7COkJV9GmG79Rkovz7lW8anPzhWYpEmEqc9dzexIxtVxriC8Z+Occy51Hmycc86lzk+jOeecS533bJxzzqXOg41zzrnUebBxzjmXOg82zjnnUufBxjnnXOo82DjnnEvd3wBrIMdw5UbqbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inference with sampling on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -572.907471, err = 0.015800\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "\n",
    "\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.eval(x, y) # \n",
    "    \n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian [IGNORE THIS FOR NOW]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)\n",
    "\n",
    "# np.save('MNIST_test_targets.npy', y_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:83: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind =35\n",
    "Nsamples = 100\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 10), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "cost, err, probs = net.eval(torch.from_numpy(ims), torch.from_numpy(y))\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9', 'confianza'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('angulo de rotacion')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(ims.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    net.set_mode_train(False)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    cost, err, probs = net.eval(torch.from_numpy(ims), torch.from_numpy(y)) # , logits=True\n",
    "\n",
    "    predictions = probs.numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "          \n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('rotation angle')\n",
    "lgd = plt.legend(['correct class', 'posterior predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aa = net.model.state_dict()\n",
    "net.model.load_state_dict(aa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2392800,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 2392800')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "weight_vector = net.get_weight_samples()\n",
    "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "symlim = 0.7\n",
    "\n",
    "lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "\n",
    "sns.distplot(weight_vector, 50, norm_hist=False, label=name, ax=ax)\n",
    "ax.set_xlim((-symlim, symlim))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d' % (len(weight_vector)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
